The Application of a Marine Weather Data Reconstruction Model Based on Deep Super-Resolution in Ship Route Optimization by Shangfu Li, Junfu Yuan, Zhizheng Wu

“…Firstly, the model uses a convolutional neural network to extract features from wind speed and wave height data. …”
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CyclicAugment: Optimized Medical Image Analysis via Adaptive Augmentation Intensity by Min-Jun Kim, Jung-Woo Chae, Hyun-Chong Cho

“…Our framework achieved a maximum accuracy improvement of 8.8%, reaching a peak performance of 0.927 and thereby demonstrating its effectiveness in enhancing medical image analysis.…”
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Detecting Lameness in Dairy Cows Based on Gait Feature Mapping and Attention Mechanisms by Xi Kang, Junjie Liang, Qian Li, Gang Liu

“…This study presents an integrated computer vision and deep-learning framework for dairy cattle lameness detection and severity classification. …”
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Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection by Sunghyuk Lee, Donghwan Roh, Jaehak Yu, Daesung Moon, Jonghyuk Lee, Ji-Hoon Bae

“…To address these limitations, this study proposes a deep learning-based intrusion detection framework that employs feature fusion through incremental transfer learning between source and target domains. …”
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Global-Frequency-Domain Network: A Semantic Segmentation Method for High-Resolution Remote Sensing Images Based on Fine-Grained Feature Extraction and Global Context Integration by Ye Zhou, Mingyue Zhang, Yechenzi Wang

“…Hence, we propose a novel global-frequency-domain network (GFDNet) designed for the semantic segmentation of high-resolution remote sensing images. The GFDNet framework incorporates a global-frequency-domain feature module that employs a learnable global filter to extract contextual information, leveraging the Fourier transform to capture both the spatial- and frequency-domain features. …”
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Deep learning-based strategies for evaluating and enhancing university teaching quality by Ying Gao

“…This study aims to address these issues by leveraging deep learning techniques, specifically Convolutional Neural Networks (CNNs), to accurately assess and enhance the quality of university teaching. …”
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DeepLASD countermeasure for logical access audio spoofing by Hamed Al-Tairi, Ali Javed, Tasawer Khan, Abdul Khader Jilani Saudagar

“…These findings affirm that the proposed end-to-end anti-spoofing framework enhances security and detection capabilities in voice authentication systems.…”
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Enhanced Face Detection Using Multi-Cascade Face Detection and Deep Ladder Neural Network by Pande Anshul, Voditel Priti

“…This hybrid system is a combination of Multi-task Cascaded Convolutional Neural Networks (MTCNN). This combined system use Deep Ladder Imputation Networks (DLIN). …”
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Investigation of deep learning approaches for automated damage diagnostics in fiber metal laminates using Detectron2 and SAM by Sanjeev Kumar, Stefan Bosse, Chirag Shah

“…This study proposes an automated approach to detect, segment, reconstruct, and characterize the damages in FML plates using state-of-the-art deep learning models: the Segment Anything Model (SAM) and the Mask Region-based Convolutional Neural Network (Mask R-CNN) implemented by the Detectron2 framework. …”
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Vision transformer-based diagnosis of lumbar disc herniation with grad-CAM interpretability in CT imaging by Qingsong Chu, Xingyu Wang, Hao Lv, Yao Zhou, Ting Jiang

“…Abstract Background In this study, a computed tomography (CT)-vision transformer (ViT) framework for diagnosing lumbar disc herniation (LDH) was proposed for the first time by taking advantage of the multidirectional advantages of CT and a ViT. …”
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A Multi-Scale Interpretability-Based PET-CT Tumor Segmentation Method by Dangui Yang, Yetong Wang, Yimeng Ma, Houqun Yang

“…To address this, we propose a tumor segmentation framework based on a multi-scale interpretability module (MSIM). …”
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Machine learning for base transceiver stations power failure prediction: A multivariate approach by Sofia Ahmed, Tsegamlak Terefe, Dereje Hailemariam

“…This paper proposes a machine-learning-based framework for preemptive BTS power failure prediction using multivariate time-series data from power and environmental monitoring systems. …”
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Micro-expression spotting based on multi-modal hierarchical semantic guided deep fusion and optical flow driven feature integration by Haolin Chang, Zhihua Xie, Fan Yang

“…To address this issue, this paper proposes a multi-scale hierarchical semantic-guided end-to-end multimodal fusion framework based on Convolutional Neural Network (CNN)-Transformer for MES, named MESFusion. …”
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Diagnosis of Alzheimer's disease using non-linear features of ERP signals through a hybrid attention-based CNN-LSTM model by Elias Mazrooei Rad, Sayyed Majid Mazinani, Seyyed Ali Zendehbad

“…In this study, a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model is proposed for the diagnosis of Alzheimer’s disease (AD) from the Event-Related Potential (ERP) signals obtained from the Electroencephalogram (EEG) data. …”
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AHN-YOLO: A Lightweight Tomato Detection Method for Dense Small-Sized Features Based on YOLO Architecture by Wenhui Zhang, Feng Jiang

“…Convolutional neural networks (CNNs) are increasingly applied in crop disease identification, yet most existing techniques are optimized solely for laboratory environments. …”
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Face Detection and Segmentation Based on Improved Mask R-CNN by Kaihan Lin, Huimin Zhao, Jujian Lv, Canyao Li, Xiaoyong Liu, Rongjun Chen, Ruoyan Zhao

“…Deep convolutional neural networks have been successfully applied to face detection recently. …”
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SwinTCS: A Swin Transformer Approach to Compressive Sensing with Non-Local Denoising by Xiuying Li, Haoze Li, Hongwei Liao, Zhufeng Suo, Xuesong Chen, Jiameng Han

“…In response to the challenges presented by traditional CS reconstruction methods, such as boundary artifacts and limited robustness, we propose a novel hierarchical deep learning framework, SwinTCS, for CS-aware image reconstruction. …”
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SOH Estimation Method for Lithium-Ion Batteries Using Partial Discharge Curves Based on CGKAN by Shengfeng He, Wenhu Qin, Zhonghua Yun, Chao Wu, Chongbin Sun

“…Next, a SOH estimation framework based on the CGKAN model is developed, where 1-Dimensional-Convolutional Neural Networks (1D-CNN) are used to extract deep features from the original data, Bidirectional Gated Recurrent Unit (BiGRU) captures the bidirectional dependencies of the time series, and Kolmogorov–Arnold Networks (KAN) enhances the modeling of complex nonlinear features through its nonlinear mapping capabilities, thereby improving the accuracy of SOH estimation. …”
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RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture by Jinye Gao, Jun Sun, Xiaohong Wu, Chunxia Dai

“…This study presents RDM-YOLO, a computationally efficient deep learning framework derived from YOLOv5s architecture, specifically designed for the automated detection of three essential behaviors (resting, wriggling, and eating) in fourth instar silkworms. …”
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Gait Recognition via Enhanced Visual–Audio Ensemble Learning with Decision Support Methods by Ruixiang Kan, Mei Wang, Tian Luo, Hongbing Qiu

“…This setup lays a solid foundation for subsequent methods and updating strategies. The core framework consists of enhanced ensemble learning methods and Dempster–Shafer Evidence Theory (D-SET). …”
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